R package for animal behavior classification from accelerometer data-rabc

Increasingly, animal behavior studies are enhanced through the use of accelerometry. To allow translation of raw accelerometer data to animal behaviors requires the development of classifiers. Here, we present the rabc (r for animal behavior classification) package to assist researchers with the interactive development of such animal behavior classifiers in a supervised classification approach. The package uses datasets consisting of accelerometer data with their corresponding animal behaviors (e.g., for triaxial accelerometer data along the x, y and z axes arranged as x, y, z, x, y, z, horizontal ellipsis , behavior). Using an example dataset collected on white stork (Ciconia ciconia), we illustrate the workflow of this package, including accelerometer data visualization, feature calculation, feature selection, feature visualization, extreme gradient boost model training, validation, and, finally, a demonstration of the behavior classification results.

Automated summary

The study introduces a package called rabc to assist researchers in developing animal behavior classifiers using supervised classification approach. This package includes workflow for accelerometer data visualization, feature calculation, selection, extreme gradient boost model training, validation, and demonstration of behavior classification results using an example dataset on white stork.